Abstract: An automated security infrastructure is disclosed that includes security agents that are designed to analyze security issues. The security agents process events received from event-messages, and records data associated with a security issue in a ticket. Security and management personnel are kept informed based on notification subscription lists. Assigned security personnel's progress in resolving outstanding security issues is monitored until those issues are resolved.

Abstract: A method for identifying high-risk orders associated with a customer that includes the steps of: receiving an order that includes a customer number, an order type and a plurality of entries; identifying a customer contract based on the order type and the customer number, the customer contract having a plurality of terms that correspond to the entries; comparing the entries to corresponding terms using rules; and identifying a high-risk order containing at least one error from the comparison. The method can also include assigning a result code or a result text to the order, which corresponds to the errors that are identified, and changing the rule set by adding or removing rules.

Abstract: An automated security infrastructure is disclosed that includes security agents that are designed to analyze security issues. The security agents process events received from event-messages, and records data associated with a security issue in a ticket. Security and management personnel are kept informed based on notification subscription lists. Assigned security personnel's progress in resolving outstanding security issues is monitored until those issues are resolved.

Abstract: An automated security infrastructure is disclosed that includes security agents that are designed to analyze security issues. The security agents process events received from event-messages, and records data associated with a security issue in a ticket. Security and management personnel are kept informed based on notification subscription lists. Assigned security personnel's progress in resolving outstanding security issues is monitored until those issues are resolved.

Abstract: A method and apparatus for determining the accuracy limit of a learning machine for predicting path performance degradation imposed by the quality of the path performance data is disclosed. A plurality of learning machines of increasing capacity are trained using training data and tested using test data, and the training error rates and test error rates are calculated. The asymptotic error rates of the learning machines are calculated and compared. When the change in asymptotic error rate falls below a certain rate, the asymptotic error rate estimates the accuracy limit for a learning machine for predicting path performance degradation. The accuracy limit is derived from insufficiencies in the path performance data and is applicable to any learning machine trained on and applied to the path performance data, regardless of the complexity of the learning machine or the size of the training data set.